How to Check if Your GPU is Being Used in TensorFlow

How to Check if Your GPU is Being Used in TensorFlow

If you’re using TensorFlow, you may be wondering if your GPU is being used to its fullest potential. Here’s how to check if your GPU is being used in TensorFlow.

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Introduction

TensorFlow is a powerful tool for deep learning, but it can be difficult to get started. In this tutorial, we’ll show you how to check if your GPU is being used in TensorFlow, and how to ensure that it is being used.

What is TensorFlow?

TensorFlow is a powerful tool for machine learning and deep learning, but it can be challenging to get started. In this guide, we’ll show you how to check if your GPU is being used by TensorFlow so you can make the most of your resources.

What is a GPU?

GPUs (graphics processing units) are specialized computer chips that can rapidly carry out repetitive tasks, making them ideal for processing large amounts of data. TensorFlow can take advantage of the power of GPUs to accelerate its operations, and in many cases, training a model with TensorFlow can be significantly faster on a GPU than on a CPU.

To see if your system is using a GPU, you can use the following code:

“`
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
“`

How to Check if Your GPU is Being Used in TensorFlow

TensorFlow is a powerful tool for machine learning, but it can be difficult to get the most out of your GPU. One way to check if your GPU is being used by TensorFlow is to open up a terminal and run the following command:

“`
nvidia-smi
“`

This will show you a list of all the GPUs on your system, as well as their utilization. If you see that your GPU is being used by TensorFlow, then you know that it’s being utilized effectively.

Why Would You Want to Check if Your GPU is Being Used in TensorFlow?

There are several reasons you might want to know if your GPU is being used in TensorFlow. For one, you may be training a model that is resource-intensive and want to make sure that your GPU is being utilized to its fullest potential. Additionally, if you are using a shared GPU, you may want to check that other users are not hoggin all of the available resources.

Fortunately, there is a simple way to check if your GPU is being used in TensorFlow. Just open up a terminal and type the following command:

“`
nvidia-smi
“`

If your GPU is being used by TensorFlow, you should see something like this:

![Image of NVIDIA-SMI Output](https://i.imgur.com/LrSWtaU.png)

How to Check if Your GPU is Being Used in TensorFlow (Continued)

One way to check if your GPU is being used by TensorFlow is to run the following code:

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

If you see something like the following output, then your GPU is being recognized by TensorFlow and is being used:

[name: “/device:CPU:0”
device_type: “CPU”
memory_limit: 268435456
locality {
}
incarnation: 10959178921497540072, name: “/device:GPU:0”
device_type: “GPU”
memory_limit: 315767664971586070146497156473512576338058464167static 0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000eval memory cuda devices gpu list tensorflow libdevice pdb`

What to do if Your GPU is Not Being Used in TensorFlow

If your system does not have a NVIDIA® GPU, you can still use TensorFlow, but you must use the CPU only. Your computer’s CPU must be a 64-bit processor. If your CPU is not 64-bit, upgrade to a more recent processor.

To check if your GPU is being used in TensorFlow:

1. Open Task Manager and select the Processes tab.
2. If you are using a Windows® operating system, open Task Manager and select the Performance tab. Then, in the upper left corner of the window, select View > Select Columns and ensure that PID is checked. This will add a column labeled PID to the Processes tab.
3. Look for a process on your task manager called tensorflow-gpu . If this process is present but uses 0% GPU, then you are likely not using your GPU because your TensorFlow version was not compiled to use CUDA or cuDNN. Please see the specific instructions below on how to install TensorFlow with GPU support for your operating system:

-Linux*: Install TensorFlow with GPU support by following the instructions here: https://www.tensorflow.org/install/gpu#linux . For best performance, be sure to install CUDA® Toolkit 9.0 and cuDNN 7..4 for CUDA Toolkit 9..0 .

-macOS*: Install TensorFlow with GPU support by following the instructions here: https://www.tensorflow.org/install/gpu#macos . For best performance, be sure to install CUDA® Toolkit 10..1 and cuDNN 7..4 for CUDA Toolkit 10..1 .

-Windows*: Install TensorFlow with GPU support by following the instructions here: https://www.tensorflow.org/install/gpu#windows . For best performance, be sure to install CUDA® Toolkit 10..1 and cuDNN 7..4 for CUDA Toolkit 10..1 .

4 If this process is present and uses 100% GPU but you are still getting slow performance, then it is likely that you are using an old graphics card (GPU). Please see the specific instructions below on how to install TensorFlow with GPU support for your operating system:

-Linux*: Upgrade your graphics card drivers by following the instructions here: https://www.nvidia.com/Download/index2=true&lang=en-us . Be sure to choose Linux as your operating system when prompted during driver installation..

-macOS*: Upgrade your graphics card drivers by following the instructions here: https://www.nvidia.com/Download/index2=true&lang=en-us . Be sure to choose macOS as your operating system when prompted during driver installation..

-Windows*: Upgrade your graphics card drivers by following the instructions here: https://www.nvidia

Conclusion

If you’re not sure if your GPU is being used by TensorFlow, don’t worry – there are a few ways to check.

One way is to open up your Task Manager (on Windows) or Activity Monitor (on Mac), and look for a process called “python.exe” (or “python3.6” if you’re using Python 3.6). If you see that process using a lot of your CPU or GPU resources, then it’s likely that TensorFlow is using your GPU.

Another way to check is to run the following code in a Python script:

import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

The line “sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))” configures TensorFlow to log which devices (CPU or GPU) it uses for each operation it performs, so running that code will print out which devices are being used for each operation.

References

In order to check if your GPU is being used by TensorFlow, you can use the following code:

”’
import tensorflow as tf
tf.test.gpu_device_name()
”’

If your GPU is being used by TensorFlow, you should see a non-empty string returned by the code above.

Further Reading

If you’re not sure if your GPU is being used by TensorFlow, the most straightforward way to check is to run the following code:

import tensorflow as tf
sess = tf.Session()
# Check if the GPUs is available to TensorFlow
print(“Num GPUs Available: “, sess.list_devices())

Keyword: How to Check if Your GPU is Being Used in TensorFlow

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